1 About

1.1 Contributions

Please note that authorship is alphabetical. Contributions are listed below - see github for details and who to blame for what :-).

Ben Anderson (b.anderson@soton.ac.uk @dataknut)

1.2 Code

https://github.com/CfSOtago/airQual/commits/master

1.3 Citation

If you wish to refer to any of the material from this report please cite as:

Anderson, B., (2020) Air Quality in Southampton (UK): Exploring the data for 2020 , University of Southampton: Southampton, UK.

Report circulation:

Public

Report purpose:

to explore (official) Southampton City Council Air Quality data (http://southampton.my-air.uk)

2 Introduction

Data downloaded from http://southampton.my-air.uk. See also https://www.southampton.gov.uk/environmental-issues/pollution/air-quality/.

Southampton City Council collects various forms of air quality data at the sites shown in 2.1. WHO publishes information on the health consequences and “acceptable” exposure levels for each of these.

lDT <- data.table::melt(origDataDT, id.vars = c("site", "dateTimeUTC"), measure.vars = c("co", "no2", "nox", 
    "oz", "pm10", "pm2_5", "so2"), value.name = "value"  # varies 
)

lDT[, `:=`(obsDate, lubridate::date(dateTimeUTC))]

# remove NA lDT <- lDT[!is.na(value)]

t <- lDT[!is.na(value), .(from = min(dateTimeUTC), to = max(dateTimeUTC), nObs = .N), keyby = .(site, variable)]

kableExtra::kable(t, caption = "Dates data != NA available by site and measure", digits = 2) %>% kable_styling()

Table 2.1: Dates data != NA available by site and measure
site	variable	from	to	nObs
Southampton - A33 Roadside (near docks, AURN site)	no2	2020-01-01 01:00:00	2020-03-23 17:00:00	1924
Southampton - A33 Roadside (near docks, AURN site)	pm10	2020-01-01 01:00:00	2020-03-23 16:00:00	1779
Southampton - Background (near city centre, AURN site)	no2	2020-01-01 01:00:00	2020-03-23 17:00:00	1946
Southampton - Background (near city centre, AURN site)	oz	2020-01-01 01:00:00	2020-03-23 17:00:00	1934
Southampton - Background (near city centre, AURN site)	pm10	2020-01-01 01:00:00	2020-03-23 17:00:00	1959
Southampton - Background (near city centre, AURN site)	pm2_5	2020-01-01 01:00:00	2020-03-23 17:00:00	1959
Southampton - Background (near city centre, AURN site)	so2	2020-01-01 01:00:00	2020-03-23 17:00:00	1933
Southampton - Onslow Road (near RSH)	no2	2020-01-01 01:00:00	2020-03-23 17:00:00	1880
Southampton - Onslow Road (near RSH)	nox	2020-01-01 01:00:00	2020-03-23 17:00:00	1880
Southampton - Victoria Road (Woolston)	no2	2020-01-01 01:00:00	2020-03-23 17:00:00	1859
Southampton - Victoria Road (Woolston)	nox	2020-01-01 01:00:00	2020-03-23 17:00:00	1859

3 Summarise data

Summarise previously downloaded and processed data… Note that this may not be completely up to date.

t <- lDT[, .(mean = mean(value, na.rm = TRUE)), keyby = .(site, variable)]

kableExtra::kable(t, caption = "Mean values per site (NaN indicates not measured)") %>% kable_styling()

Table 3.1: Mean values per site (NaN indicates not measured)
site	variable	mean
Southampton - A33 Roadside (near docks, AURN site)	co	NaN
Southampton - A33 Roadside (near docks, AURN site)	no2	30.587630
Southampton - A33 Roadside (near docks, AURN site)	nox	NaN
Southampton - A33 Roadside (near docks, AURN site)	oz	NaN
Southampton - A33 Roadside (near docks, AURN site)	pm10	17.101686
Southampton - A33 Roadside (near docks, AURN site)	pm2_5	NaN
Southampton - A33 Roadside (near docks, AURN site)	so2	NaN
Southampton - Background (near city centre, AURN site)	co	NaN
Southampton - Background (near city centre, AURN site)	no2	25.938027
Southampton - Background (near city centre, AURN site)	nox	NaN
Southampton - Background (near city centre, AURN site)	oz	48.800672
Southampton - Background (near city centre, AURN site)	pm10	14.892496
Southampton - Background (near city centre, AURN site)	pm2_5	9.405207
Southampton - Background (near city centre, AURN site)	so2	2.485101
Southampton - Bitterne	co	NaN
Southampton - Bitterne	no2	NaN
Southampton - Bitterne	nox	NaN
Southampton - Bitterne	oz	NaN
Southampton - Bitterne	pm10	NaN
Southampton - Bitterne	pm2_5	NaN
Southampton - Bitterne	so2	NaN
Southampton - Onslow Road (near RSH)	co	NaN
Southampton - Onslow Road (near RSH)	no2	36.296968
Southampton - Onslow Road (near RSH)	nox	72.896489
Southampton - Onslow Road (near RSH)	oz	NaN
Southampton - Onslow Road (near RSH)	pm10	NaN
Southampton - Onslow Road (near RSH)	pm2_5	NaN
Southampton - Onslow Road (near RSH)	so2	NaN
Southampton - Redbridge	co	NaN
Southampton - Redbridge	no2	NaN
Southampton - Redbridge	nox	NaN
Southampton - Redbridge	oz	NaN
Southampton - Redbridge	pm10	NaN
Southampton - Redbridge	pm2_5	NaN
Southampton - Redbridge	so2	NaN
Southampton - Victoria Road (Woolston)	co	NaN
Southampton - Victoria Road (Woolston)	no2	25.121248
Southampton - Victoria Road (Woolston)	nox	54.818397
Southampton - Victoria Road (Woolston)	oz	NaN
Southampton - Victoria Road (Woolston)	pm10	NaN
Southampton - Victoria Road (Woolston)	pm2_5	NaN
Southampton - Victoria Road (Woolston)	so2	NaN

Table 3.1 gives an indication of the availability of the different measures.

4 Analysis

In this section we present graphical analysis of the previoulsy downloaded data. Note this is just a snapshot of the data available.

4.1 Nitrogen Dioxide

yLab <- "Nitrogen Dioxide (ug/m3)"
dt <- lDT[variable == "no2"]
t <- dt[, .(mean = mean(value, na.rm = TRUE), sd = sd(value, na.rm = TRUE), min = min(value, na.rm = TRUE), 
    max = max(value, na.rm = TRUE)), keyby = .(site)]
kableExtra::kable(t, caption = "Summary of NO2 data") %>% kable_styling()

Table 4.1: Summary of NO2 data
site	mean	sd	min	max
Southampton - A33 Roadside (near docks, AURN site)	30.58763	20.59193	0.0	102.9
Southampton - Background (near city centre, AURN site)	25.93803	16.51945	2.3	130.6
Southampton - Bitterne	NaN	NA	Inf	-Inf
Southampton - Onslow Road (near RSH)	36.29697	19.74920	3.3	125.2
Southampton - Redbridge	NaN	NA	Inf	-Inf
Southampton - Victoria Road (Woolston)	25.12125	17.37546	0.0	106.7

Table 4.1 suggests that there may be a few (0) negative values. These are summarised in 4.2 while Figure 4.1 shows the availability and levels of the pollutant data over time.

t <- head(dt[value < 0], 10)
kableExtra::kable(t, caption = "Negative NO2 values (up to first 10)") %>% kable_styling()

Table 4.2: Negative NO2 values (up to first 10)
site	dateTimeUTC	variable	value	obsDate

t <- table(dt[value < 0]$site)
kableExtra::kable(t, caption = "Negative NO2 values (count by site)") %>% kable_styling()

Table 4.2: Negative NO2 values (count by site)
Freq

# dt,xvar, yvar,fillVar, yLab
p <- makeTilePlot(dt, xVar = "dateTimeUTC", yVar = "site", fillVar = "value", yLab = yLab)

p

Figure 4.1: Nitrogen Dioxide data availability and levels over time

Figure 4.2 shows daily mean values for all sites over time and includes smoother trend lines for each site.

plotDT <- dt[!is.na(value), .(mean = mean(value, na.rm = TRUE)),
             keyby = .(obsDate, site)]

p <- makeDotPlot(plotDT, 
                 xVar = "obsDate", 
                 yVar = "mean", 
                 byVar = "site", 
                 yLab = paste0("Mean daily ", yLab)
                 )

p <- p +
  geom_smooth() + # add smoothed line
  labs(caption = "Trend line = Generalized additive model (gam) with integrated smoothness estimation")

if(doPlotly){
  p
  plotly::ggplotly(p + xlim(xlimMinDate, xlimMaxDate)) # interactive, xlimited # interactive
} else {
  p
}

Figure 4.2: Nitrogen Dioxide levels, Southampton (daily mean

dt <- dt[dateTimeUTC > oneYearAgo]

t <- dt[value > hourlyNo2Threshold_WHO][order(-value)]

kableExtra::kable(caption = paste0("Values greater than WHO threshold (NO2 > ", hourlyNo2Threshold_WHO, 
    ", last 12 months)"), head(t, 10)) %>% kable_styling()

Table 4.3: Values greater than WHO threshold (NO2 > 200, last 12 months)
site	dateTimeUTC	variable	value	obsDate

p <- makeDotPlot(dt[!is.na(value)], xVar = "dateTimeUTC", yVar = "value", byVar = "site", yLab = yLab)

p <- p + geom_hline(yintercept = hourlyNo2Threshold_WHO) + labs(caption = "Reference line = WHO hourly threshold")


plotly::ggplotly(p)  # for interaction

Figure 4.3: Nitrogen Dioxide levels, Southampton (hourly, last 12 months)

Figure 4.3 shows hourly values for all sites for the last 12 months. In this period there were 0 hours when the hourly Nitrogen Dioxide level breached the relevant WHO hourly threshold (200). The worst 10 cases (if any) are shown in Table 4.3.

Clearly there are winter peaks, the mean daily values show less variance (and less extremes) than the hourly data and there has also been a decreasing trend over time.

4.2 PM 10

PM 10 data: has more sensors and wider coverage than PM2.5

yLab <- "PM 10 (ug/m3)"
dt <- lDT[variable == "pm10"]

t <- dt[, .(mean = mean(value, na.rm = TRUE), sd = sd(value, na.rm = TRUE), min = min(value, na.rm = TRUE), 
    max = max(value, na.rm = TRUE)), keyby = .(site)]
kableExtra::kable(t, caption = "Summary of pm10 data") %>% kable_styling()

Table 4.4: Summary of pm10 data
site	mean	sd	min	max
Southampton - A33 Roadside (near docks, AURN site)	17.10169	11.11763	0.0	99.0
Southampton - Background (near city centre, AURN site)	14.89250	8.81082	0.8	76.4
Southampton - Bitterne	NaN	NA	Inf	-Inf
Southampton - Onslow Road (near RSH)	NaN	NA	Inf	-Inf
Southampton - Redbridge	NaN	NA	Inf	-Inf
Southampton - Victoria Road (Woolston)	NaN	NA	Inf	-Inf

Table 4.4 suggests that there may be a few (0) negative values. These are shown in 4.5 while 4.4 shows data availability and PM 10 levels over time at each site.

t <- head(dt[value < 0], nrow(dt[value < 0]))
kableExtra::kable(head(t), caption = "Negative PM10 values - first 6") %>% kable_styling()

Table 4.5: Negative PM10 values - first 6
site	dateTimeUTC	variable	value	obsDate

p <- makeTilePlot(lDT[variable == "pm10"], xVar = "dateTimeUTC", yVar = "site", fillVar = "value", yLab = yLab)

p

Figure 4.4: Availability and level of PM 10 data over time

plotDT <- dt[!is.na(value), .(mean = mean(value, na.rm = TRUE)),
             keyby = .(obsDate, site)]

extremePm10Daily <- plotDT[mean > dailyPm10Threshold_WHO][order(-mean)]

kableExtra::kable(caption = paste0("10 highest values greater than WHO threshold (PM 10 > ", 
                                   dailyPm10Threshold_WHO , ")"), 
                  digits = 2,
                  head(extremePm10Daily, 10)) %>%
  kable_styling()

Table 4.6: 10 highest values greater than WHO threshold (PM 10 > 50)
obsDate	site	mean

p <- makeDotPlot(plotDT, 
                 xVar = "obsDate", 
                 yVar = "mean", 
                 byVar = "site", 
                 yLab = paste0("Mean daily ", yLab)
                 )

p <- p + 
  geom_hline(yintercept = dailyPm10Threshold_WHO) +
  geom_smooth() + # add smoothed line
  labs(caption = "Trend line = Generalized additive model (gam) with integrated smoothness estimation\nReference line = WHO PM10 mean daily threshold")


plotly::ggplotly(p) # interactive

Figure 4.5: PM10 levels, Southampton (daily mean)

nDaysOverThreshold <- uniqueN(extremePm10Daily$obsDate)
nDays <- uniqueN(plotDT$obsDate) # need to count days not site-days

Figure 4.5 shows daily values for all sites across the entire dataset and indicates the 0 days (0%) that breached the WHO PM10 daily mean exposure threshold (50) - see Table 4.6.

dt <- dt[dateTimeUTC > oneYearAgo]
t <- dt[value > 100][order(-value)]

kableExtra::kable(caption = "10 highest hourly values (PM 10 > 100)", head(t)) %>% kable_styling()

Table 4.7: 10 highest hourly values (PM 10 > 100)
site	dateTimeUTC	variable	value	obsDate

p <- makeDotPlot(dt[!is.na(value)], xVar = "dateTimeUTC", yVar = "value", byVar = "site", yLab = yLab)

p <- p + labs(caption = "NB: There is no WHO PM10 hourly threshold")

if (doPlotly) {
    p
    plotly::ggplotly(p + xlim(xlimMinDateTime, xlimMaxDateTime))  # interactive, xlimited # interactive
} else {
    p
}

Figure 4.6: PM10 levels, Southampton (hourly, last 12 months)

Figure 4.6 shows hourly PM 10 values for all sites over the last 12 months and suggests there may be outliers (see Table 4.7).

4.3 PM 2.5

yLab <- "PM 2.5 (ug/m3)"
dt <- lDT[variable == "pm2_5"]
t <- dt[!is.na(value), .(mean = mean(value, na.rm = TRUE), sd = sd(value, na.rm = TRUE), min = min(value, 
    na.rm = TRUE), max = max(value, na.rm = TRUE)), keyby = .(site)]
kableExtra::kable(t, caption = "Summary of pm2_5 data") %>% kable_styling()

Table 4.8: Summary of pm2_5 data
site	mean	sd	min	max
Southampton - Background (near city centre, AURN site)	9.405207	6.784957	0.4	48.5

Table 4.8 suggests that there may be a few (0) negative values. These are shown in Table 4.9 while Figure 4.7 shows data availability and PM 2.5 levels over time at each site.

t <- head(dt[value < 0], nrow(dt[value < 0]))
kableExtra::kable(head(t), caption = "Negative pm2_5 values - first 6") %>% kable_styling()

Table 4.9: Negative pm2_5 values - first 6
site	dateTimeUTC	variable	value	obsDate

p <- makeTilePlot(dt, xVar = "dateTimeUTC", yVar = "site", fillVar = "value", yLab = yLab)

p

Figure 4.7: Availability and level of PM 10 data over time

plotDT <- dt[!is.na(value), .(mean = mean(value, na.rm = TRUE)),
             keyby = .(obsDate, site)]

extremePm25Daily <- plotDT[mean > dailyPm2.5Threshold_WHO][order(-mean)]

kableExtra::kable(caption = paste0("6 highest values greater than WHO threshold (PM 2.5 > ", 
                                   dailyPm2.5Threshold_WHO , ")"), 
                  digits = 2,
                  head(extremePm25Daily)) %>%
  kable_styling()

Table 4.10: 6 highest values greater than WHO threshold (PM 2.5 > 25)
obsDate	site	mean
2020-01-21	Southampton - Background (near city centre, AURN site)	32.60
2020-01-20	Southampton - Background (near city centre, AURN site)	26.30
2020-01-22	Southampton - Background (near city centre, AURN site)	26.27

p <- makeDotPlot(plotDT, 
                 xVar = "obsDate", 
                 yVar = "mean", 
                 byVar = "site", 
                 yLab = paste0("Mean daily ", yLab)
                 )

p <- p + 
  geom_hline(yintercept = dailyPm2.5Threshold_WHO) +
  geom_smooth() + #add smoothed line
  labs(caption = "Trend line = Generalized additive model (gam) with integrated smoothness estimation\nReference line = WHO daily PM2.5 threshold")


if(doPlotly){
  p
  plotly::ggplotly(p + xlim(xlimMinDate, xlimMaxDate)) # interactive, xlimited # interactive
} else {
  p
}

Figure 4.8: PM2_5 levels, Southampton (daily mean)

nDaysOverThreshold <- uniqueN(extremePm25Daily$obsDate)
nDays <- uniqueN(plotDT$obsDate) # need to count days not site-days

Figure 4.8 shows daily values for all sites across the dataset and indicates that the WHO PM2_5 daily mean exposure threshold (25) was breached on 3 days (3.6 %). The 6 worst cases are shown in Table 4.10.

dt <- dt[dateTimeUTC > oneYearAgo]
t <- dt[value > 50][order(-value)]

kableExtra::kable(caption = "Extreme hourly values (PM 2.5 > 50, last 12 months, worst 6)", head(t)) %>% 
    kable_styling()

Table 4.11: Extreme hourly values (PM 2.5 > 50, last 12 months, worst 6)
site	dateTimeUTC	variable	value	obsDate

p <- makeDotPlot(dt[!is.na(value)], xVar = "dateTimeUTC", yVar = "value", byVar = "site", yLab = yLab)

p <- p + labs(caption = "NB: There is no WHO PM2.5 hourly threshold")

plotly::ggplotly(p)  # for interaction

Figure 4.9: PM2_5 levels, Southampton (hourly, last 12 months)

Figure 4.9 shows hourly values for all sites for the last 12 months while Table 4.11 reports the 6 worst hours.

5 Observations

Why are there large data holes? (see tile plots)
Nitorgen Dioxide levels appear to be trending downwards
For particulates the trend is more complex and the trends for PM10 and PM2.5 differ.
- Something happened on the 27th October 2019 at 20:00. There are spikes on all hourly PM plots (although this is masked in the daily plots). Could it have been a cruise ship leaving?
- Something else happened on the 2nd December 2019 at 21:00. Was this another ship?

6 Annex

6.1 Original data

skimr::skim(origDataDT)

Table 6.1: Data summary

Name	origDataDT
Number of rows	52560
Number of columns	10
_______________________
Column type frequency:
character	1
logical	1
numeric	7
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
site	0	1	22	54	0	6	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
co	52560	0	NaN	:

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
no	44951	0.14	21.11	35.21	-0.2	3.20	9.3	24.60	486.4	▇▁▁▁▁
no2	44951	0.14	29.47	19.15	0.0	14.20	26.0	41.10	130.6	▇▆▂▁▁
nox	48821	0.07	63.91	71.65	1.1	22.05	45.7	79.55	848.1	▇▁▁▁▁
pm10	48822	0.07	15.94	10.03	0.0	9.00	14.0	20.30	99.0	▇▂▁▁▁
oz	50626	0.04	48.80	21.98	0.0	33.95	53.9	65.70	89.4	▂▃▅▇▃
pm2_5	50601	0.04	9.41	6.78	0.4	5.10	7.7	11.30	48.5	▇▃▁▁▁
so2	50627	0.04	2.49	1.42	0.1	1.50	2.1	3.10	10.4	▇▆▂▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dateTimeUTC	0	1	2020-01-01 01:00:00	2020-12-31	2020-07-01 12:30:00	8760

6.2 Long form of original data

skimr::skim(lDT)

Table 6.2: Data summary

Name	lDT
Number of rows	367920
Number of columns	5
_______________________
Column type frequency:
character	1
Date	1
factor	1
numeric	1
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
site	0	1	22	54	0	6	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
obsDate	0	1	2020-01-01	2020-12-31	2020-07-01	366

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
variable	0	1	FALSE	7	co: 52560, no2: 52560, nox: 52560, oz: 52560

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
value	347008	0.06	30.62	38.75	0	8.7	20	42.1	848.1	▇▁▁▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dateTimeUTC	0	1	2020-01-01 01:00:00	2020-12-31	2020-07-01 12:30:00	8760

6.3 Nitrogen Dioxide

t <- lDT[variable == "no2"]

skimr::skim(t)

Table 6.3: Data summary

Name	t
Number of rows	52560
Number of columns	5
_______________________
Column type frequency:
character	1
Date	1
factor	1
numeric	1
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
site	0	1	22	54	0	6	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
obsDate	0	1	2020-01-01	2020-12-31	2020-07-01	366

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
variable	0	1	FALSE	1	no2: 52560, co: 0, nox: 0, oz: 0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
value	44951	0.14	29.47	19.15	0	14.2	26	41.1	130.6	▇▆▂▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dateTimeUTC	0	1	2020-01-01 01:00:00	2020-12-31	2020-07-01 12:30:00	8760

t <- lDT[variable == "no2" & value > hourlyNo2Threshold_WHO][order(-value)]

kableExtra::kable(caption = paste0("Values greater than WHO threshold (NO2 > ", hourlyNo2Threshold_WHO, 
    ")"), t) %>% kable_styling()

Table 6.4: Values greater than WHO threshold (NO2 > 200)
site	dateTimeUTC	variable	value	obsDate

6.4 PM 10

t <- lDT[variable == "pm10"]

skimr::skim(t)

Table 6.5: Data summary

Name	t
Number of rows	52560
Number of columns	5
_______________________
Column type frequency:
character	1
Date	1
factor	1
numeric	1
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
site	0	1	22	54	0	6	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
obsDate	0	1	2020-01-01	2020-12-31	2020-07-01	366

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
variable	0	1	FALSE	1	pm1: 52560, co: 0, no2: 0, nox: 0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
value	48822	0.07	15.94	10.03	0	9	14	20.3	99	▇▂▁▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dateTimeUTC	0	1	2020-01-01 01:00:00	2020-12-31	2020-07-01 12:30:00	8760

kableExtra::kable(caption = paste0("PM 10 values greater than WHO threshold (NO2 > ", hourlyNo2Threshold_WHO, 
    ")"), extremePm10Daily) %>% kable_styling()

Table 6.6: PM 10 values greater than WHO threshold (NO2 > 200)
obsDate	site	mean

6.5 PM 2.5

t <- lDT[variable == "pm2_5"]

skimr::skim(t)

Table 6.7: Data summary

Name	t
Number of rows	52560
Number of columns	5
_______________________
Column type frequency:
character	1
Date	1
factor	1
numeric	1
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
site	0	1	22	54	0	6	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
obsDate	0	1	2020-01-01	2020-12-31	2020-07-01	366

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
variable	0	1	FALSE	1	pm2: 52560, co: 0, no2: 0, nox: 0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
value	50601	0.04	9.41	6.78	0.4	5.1	7.7	11.3	48.5	▇▃▁▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dateTimeUTC	0	1	2020-01-01 01:00:00	2020-12-31	2020-07-01 12:30:00	8760

kableExtra::kable(caption = paste0("PM 2.5 values greater than WHO threshold (NO2 > ", hourlyNo2Threshold_WHO, 
    ")"), extremePm25Daily) %>% kable_styling()

Table 6.8: PM 2.5 values greater than WHO threshold (NO2 > 200)
obsDate	site	mean
2020-01-21	Southampton - Background (near city centre, AURN site)	32.60417
2020-01-20	Southampton - Background (near city centre, AURN site)	26.30417
2020-01-22	Southampton - Background (near city centre, AURN site)	26.27083

7 Runtime

Report generated using knitr in RStudio with R version 3.6.3 (2020-02-29) running on x86_64-apple-darwin15.6.0 (Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64).

t <- proc.time() - startTime

elapsed <- t[[3]]

Analysis completed in 19.449 seconds ( 0.32 minutes).

R packages used:

data.table - (Dowle et al. 2015)
ggplot2 - (Wickham 2009)
here - (Müller 2017)
kableExtra - (Zhu 2018)
lubridate - (Grolemund and Wickham 2011)
plotly - (Sievert et al. 2016)
skimr - (Arino de la Rubia et al. 2017)

References

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.

Müller, Kirill. 2017. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.

Sievert, Carson, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec, and Pedro Despouy. 2016. Plotly: Create Interactive Web Graphics via ’Plotly.js’. https://CRAN.R-project.org/package=plotly.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Zhu, Hao. 2018. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

Air Quality in Southampton (UK)

Exploring the data for 2020

Ben Anderson (b.anderson@soton.ac.uk `@dataknut`)

Last run at: 2020-03-23 18:45:40 (Europe/London)

1 About

1.1 Contributions

1.2 Code

1.3 Citation

2 Introduction

3 Summarise data

4 Analysis

4.1 Nitrogen Dioxide

4.2 PM 10

4.3 PM 2.5

5 Observations

6 Annex

6.1 Original data

6.2 Long form of original data

6.3 Nitrogen Dioxide

6.4 PM 10

6.5 PM 2.5

7 Runtime

References

Air Quality in Southampton (UK)

Exploring the data for 2020

Ben Anderson (b.anderson@soton.ac.uk @dataknut)

Last run at: 2020-03-23 18:45:40 (Europe/London)

1 About

1.1 Contributions

1.2 Code

1.3 Citation

2 Introduction

3 Summarise data

4 Analysis

4.1 Nitrogen Dioxide

4.2 PM 10

4.3 PM 2.5

5 Observations

6 Annex

6.1 Original data

6.2 Long form of original data

6.3 Nitrogen Dioxide

6.4 PM 10

6.5 PM 2.5

7 Runtime

References

Ben Anderson (b.anderson@soton.ac.uk `@dataknut`)